A secant-based adaptive correction augments first-order optimizers to improve convergence speed, stability, and accuracy when training PINNs on challenging PDEs.
Physics-informed neural networks (PINN) for computational solid mechanics: Numerical frameworks and applications.Thin-Walled Struct., 205:112495, 2024
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Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks
A secant-based adaptive correction augments first-order optimizers to improve convergence speed, stability, and accuracy when training PINNs on challenging PDEs.